The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.
Person Recognition in Aerial Surveillance: A Decade Survey
Signal Processing, AI and Vision Technologies (SAIVT) Research Program（信号处理、人工智能与视觉技术研究计划）；Queensland University of Technology（昆士兰理工大学）；Department of Computer Science（计算机科学系）；Drexel University（德雷塞尔大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Michigan State University（密歇根州立大学）
Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring
Renmin University of China（中国人民大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；International Research Institute of Disaster Science (IRIDeS), Tohoku University（东北大学灾害科学国际研究所）
随着民用无人机的迅速发展，利用无人机平台进行视觉监控已经成为一个非常有吸引力的课题。目前对无人机视觉数据的研究主要集中在目标检测和跟踪任务上。然而，有限的关注已经支付给人的重新识别（ReID）的任务，已被广泛研究，在普通的监控摄像机与固定的炮位。为了便于研究航空图像中的人员ReID，我们收集了一个名为Person ReID for Aerial Imagery（PRAI-1581）的大规模空中人员ReID数据集，该数据集由1581个人员身份的39，461张图像组成。数据集的图像由两架大疆消费级无人机在离地20至60米的高度飞行拍摄，涵盖了大部分真实的无人机监控场景。此外，我们建议利用卷积特征映射的子空间池来表示输入的人物图像。我们的方法可以在航空图像中学习ReID的区分性和紧凑的特征表示，并且可以有效地以端到端的方式进行训练。我们对所提出的数据集进行了广泛的实验，实验结果表明，重新识别航空图像中的人是一个具有挑战性的问题，我们的方法对最先进的技术表现良好。我们的数据集可以通过\url{https://github.com/stormyoung/PRAI-1581}访问。
Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection Model Training in Unmanned Aerial Vehicles Surveillance Scenarios
Center for Ubiquitous Computing (UBICOMP), University of Oulu, Finland（通用计算中心（UBICOMP），奥卢大学，芬兰）；Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland（机器视觉与信号分析中心（CMVS），奥卢大学，芬兰）；Center for Wireless Communications (CWC), University of Oulu, Finland（无线通信中心（CWC），奥卢大学，芬兰）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, United States（明斯希德电气与计算机工程系，南加州大学，美国）；Department of Computer Science, University of Helsinki, Finland（计算机科学系，赫尔辛基大学，芬兰）；University of Oulu, Oulu, Finland（奥卢大学，奥卢，芬兰）；University of Helsinki, Helsinki, Finland（赫尔辛基大学，赫尔辛基，芬兰）；University of Southern California, Los Angeles, United States（南加州大学，洛杉矶，美国）
Automatic detection of aerial survey ground control points based on Yolov5-OBB
Henan University of Urban Construction（河南城建学院）；University of North Carolina（北卡罗来纳大学）；Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences（中国科学院东北地理与农业生态研究所）；Zhengzhou University（郑州大学）
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Wuhan University of Technology（武汉理工大学）；Chongqing Research Institute, Wuhan University of Technology（武汉理工大学重庆研究院）；Nanyang Technological University（南洋理工大学）
Visual place recognition for aerial imagery: A survey
Center for AI Technology (CAIT)（人工智能技术中心（CAIT））；Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术学院（Skoltech））；Saint Petersburg State University（圣彼得堡国立大学）
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；University of Otago（奥塔哥大学）；Princeton University（普林斯顿大学）；Marine Bycatch and Threats – Department of Conservation（海洋误捕与威胁——保护部门）；Antarctic Research Trust（南极研究信托）；The Tawaki Trust（塔瓦基信托）；Global Penguin Society（全球企鹅协会）
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
CISTER - Research Centre in Real-Time and Embedded Computing Systems（CISTER实时与嵌入式计算系统研究中心）；The Hong Kong Polytechnic University（香港理工大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（联邦科学与工业研究组织（CSIRO））；University of Manitoba（曼尼托巴大学）
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）；School of Transportation, Southeast University（东南大学交通学院）；Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture（北京建筑大学通用航空技术实验室）
Microsoft AI for Good Lab（微软人工智能公益实验室）；Center for Research and Formation in Artificial Intelligence (Cinfonia), Universidad de los Andes（安第斯大学人工智能研究与培训中心）；Conservation X Labs（保护X实验室）；Kirk Larsen Consulting（Kirk Larsen咨询公司）；Tanzania Wildlife Research Institute（坦桑尼亚野生动物研究所）；Alaska Department of Fish and Game（阿拉斯加渔猎局）
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学地理空间工程与科学学院）；Faculty of Geo-Information Science and Earth Observation, University of Twente（特温特大学地球信息科学与对地观测学院）；Boma and Badingilo National Parks, African Parks（博马和巴丁吉洛国家公园，非洲公园组织）；Department of Ecology and Evolutionary Biology, Princeton University（普林斯顿大学生态与进化生物学系）；Lolldaiga Hills Research Programme（洛尔迪加山研究项目）；School of Biological, Earth & Environmental Sciences, Environmental Research Institute, University College Cork（科克大学环境研究所生物、地球与环境科学学院）
SLAM-Based Navigation and Fault Resilience in a Surveillance Quadcopter with Embedded Vision Systems
Guru Gobind Singh Indraprastha University（古鲁·戈宾德·辛格·因德拉普拉斯塔大学）；Delhi Skill and Entrepreneurship University (DSEU)（德里技能与创业大学（DSEU））；Delhi Institute of Tool Engineering（德里工具工程学院）
